Citations

...ent representation. Our work is related to the previous research on text categorization, including decision trees [2], logistic regression [12], and support vector machines (SVM) reported as the best =-=[7]-=-. A number of studies have also been devoted to using semi-supervised learning techniques for text categorization, including transductive support vector machine [9], graph-based approaches [1, 13] and...

... classification; Section 5 concludes this paper with the future work. 2 Related Work This work is closely related to previous studies on exploiting user feedback information for information retrieval =-=[5, 8, 11]-=-. Unlike the previous studies in information retrieval, this study utilizes the user feedback information for text categorization. Our work also differs from the previous studies on adaptive informati...

...machines (SVM) reported as the best [7]. A number of studies have also been devoted to using semi-supervised learning techniques for text categorization, including transductive support vector machine =-=[9]-=-, graph-based approaches [1, 13] and Bayesian classifiers [4]. Our work differs from earlier research in that it uses the users’ feedback, rather than the textual content, for classification and it fo...

...he best [7]. A number of studies have also been devoted to using semi-supervised learning techniques for text categorization, including transductive support vector machine [9], graph-based approaches =-=[1, 13]-=- and Bayesian classifiers [4]. Our work differs from earlier research in that it uses the users’ feedback, rather than the textual content, for classification and it focuses on exploiting the unlabele...

...he best [7]. A number of studies have also been devoted to using semi-supervised learning techniques for text categorization, including transductive support vector machine [9], graph-based approaches =-=[1, 13]-=- and Bayesian classifiers [4]. Our work differs from earlier research in that it uses the users’ feedback, rather than the textual content, for classification and it focuses on exploiting the unlabele...

... classification; Section 5 concludes this paper with the future work. 2 Related Work This work is closely related to previous studies on exploiting user feedback information for information retrieval =-=[5, 8, 11]-=-. Unlike the previous studies in information retrieval, this study utilizes the user feedback information for text categorization. Our work also differs from the previous studies on adaptive informati...

...a relatively small number of user clusters and then represent each document by the aggregated feedback from each user cluster. In this study, we choose the probabilistic spectral clustering algorithm =-=[6]-=- because of its effectiveness and soft cluster membership assignments that is better for capturing the feedback of users with mixed interests. One difficulty in the user clustering approach is how to ...

... classification; Section 5 concludes this paper with the future work. 2 Related Work This work is closely related to previous studies on exploiting user feedback information for information retrieval =-=[5, 8, 11]-=-. Unlike the previous studies in information retrieval, this study utilizes the user feedback information for text categorization. Our work also differs from the previous studies on adaptive informati...

...class label while our work uses feedback as part of the document representation. Our work is related to the previous research on text categorization, including decision trees [2], logistic regression =-=[12]-=-, and support vector machines (SVM) reported as the best [7]. A number of studies have also been devoted to using semi-supervised learning techniques for text categorization, including transductive su...

...mploys the feedback as a class label while our work uses feedback as part of the document representation. Our work is related to the previous research on text categorization, including decision trees =-=[2]-=-, logistic regression [12], and support vector machines (SVM) reported as the best [7]. A number of studies have also been devoted to using semi-supervised learning techniques for text categorization,...

...have also been devoted to using semi-supervised learning techniques for text categorization, including transductive support vector machine [9], graph-based approaches [1, 13] and Bayesian classifiers =-=[4]-=-. Our work differs from earlier research in that it uses the users’ feedback, rather than the textual content, for classification and it focuses on exploiting the unlabeled documents to alleviate the ...

...s studies in information retrieval, this study utilizes the user feedback information for text categorization. Our work also differs from the previous studies on adaptive information filtering (e.g., =-=[10]-=-) in that the adaptive information filtering employs the feedback as a class label while our work uses feedback as part of the document representation. Our work is related to the previous research on ...